A new approach for estimation of obstructive sleep apnea syndrome |
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| Authors: | M Emin Tagluk Necmettin Sezgin |
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| Affiliation: | 1. Department of Electrical and Electronics Engineering, University of Inonu, Malatya, Turkey;2. Department of Electrical and Electronics Engineering, University of Batman, Batman, Turkey;1. Centre de Mathématiques et de Leurs Applications, Ecole Normale Supérieure de Cachan and CNRS UMR8536, 61, Avenue du Président Wilson, 94235 Cachan Cedex, France;2. School of Mathematical Sciences, University College Dublin, Belfield, Dublin 4, Ireland;3. Laboratoire de Mathématiques d’Orsay and CNRS UMR8628, Université Paris-Sud XI, Bât. 425, 91405 Orsay Cedex, France;4. Commissariat à l’Énergie Atomique et aux Énergies Alternatives, Centre de Bruyères-le-Châtel, 91297 Arpajon Cedex, France;1. Graduate School of Technology, Industrial and Social Sciences, Tokushima University, 2-1 Minamijyosanjima-cho Tokushima 770-8506, Japan;2. School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, Brisbane QLD, 4072, Australia;3. Graduate School of Advanced Technology and Science, Tokushima University, 2-1 Minamijyosanjima-cho Tokushima 770-8506, Japan;4. Department of Otorhinolaryngology, Anan Kyoei Hospital, Nakasyo, Hanoura-cho, Anan-city, Tokushima 779-1198, Japan;1. Department of Electrical and Electronics Engineering, BITS-Pilani, Hyderabad Campus, Hyderabad, 500078, India;2. Department of Electronic & Computer Engineering, Ngee Ann Polytechnic, Singapore;3. Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore;4. Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia;1. NHMRC Centre of Research Excellence CIRUS and NEUROSLEEP, Woolcock Institute of Medical Research, The University of Sydney, Sydney, Australia;3. Sydney Local Health District, Sydney, New South Wales, Australia;4. Sydney Medical School, University of Sydney, Australia;5. School of Physics, University of Sydney, Sydney, Australia;6. Department of Respiratory and Sleep Medicine, Royal Prince Alfred Hospital, Sydney, Australia;1. Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran;2. Department of Electrical Engineering, AmirKabir University of Technology, Tehran, Iran;1. Sleep Disorders Research Center, Kermanshah University of Medical Sciences (KUMS), Kermanshah, Iran;2. National Brain Mapping Center, Shahid Beheshti University (General & Medical campus), Tehran, Iran;3. Sleep and Brain Plasticity Centre, Department of Neuroimaging, IOPPN, King''s College and Imperial College, London, UK;4. Institute of Clinical Neuroscience & Medical Psychology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany;5. Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany;6. Division of Neurology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada;7. Department of Physics, Florida International University, Miami, FL, USA;8. Research Imaging Institute, University of Texas Health Science Center, San Antonio, TX, USA;9. South Texas Veterans Health Care System,San Antonio, TX 78229, USA;10. Academic Unit of Sleep and Breathing, National Heart and Lung Institute, Imperial College London, UK;11. NIHR Respiratory Disease Biomedical Research Unit at the Royal Brompton and Harefield NHS Foundation Trust and Imperial College London, UK;12. Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, Aachen, Germany |
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| Abstract: | Obstructive sleep apnea syndrome (OSAS) is a situation where repeatedly upper airway stops off while the respiratory effort continues during sleep at least for 10 s. Apart from polysomnography, many researchers have concentrated on exploring alternative methods for OSAS detection. However, not much work has been done on using non-Gaussian and nonlinear behavior of the electroencephalogram (EEG) signals. Bispectral analysis is an advanced signal processing technique particularly used for exhibiting quadratic phase-coupling that may arise between signal components with different frequencies. From this perspective, in this study, a new technique for recognizing patients with OSAS was introduced using bispectral characteristics of EEG signal and an artificial neural network (ANN). The amount of Quadratic phase coupling (QPC) in each subband of EEG (namely; delta, theta, alpha, beta and gamma) was calculated over bispectral density of EEG. Then, these QPCs were fed to the input of the designed ANN. The neural network was configured with two outputs: one for OSAS and one for estimation of normal situation. With this technique a global accuracy of 96.15% was achieved. The proposed technique could be used in designing automatic OSAS identification systems which will improve medical service. |
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